{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial for srfpython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Overview**\n",
    " + Intro : import and verify installation\n",
    " + I/ create a 1-D depth model  \n",
    " + II/ compute dispersion curves   \n",
    "     + II.1/ use in a python program  \n",
    "     + II.2/ use in command line   \n",
    " - III/ depth inversion  \n",
    " - III.1/ Program HerrMet  \n",
    " - III.2/ Application  \n",
    "     + III.2.1/ Target data  \n",
    "     + III.2.2/ Parameterization  \n",
    "     + III.2.3/ Run inversion  \n",
    "     + III.2.4/ Extract results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intro/ import and verify installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -----------------------\n",
    "# import all components of srfpython\n",
    "# -----------------------\n",
    "from srfpython import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok\n"
     ]
    }
   ],
   "source": [
    "# -----------------------\n",
    "# make sure that the fortran codes have been compiled correctly\n",
    "# -----------------------\n",
    "try:\n",
    "    check_herrmann_codes() \n",
    "    print \"ok\"\n",
    "    \n",
    "except Exception:\n",
    "    print \"compilation was not done or done on another system\"\n",
    "\n",
    "    # recompile\n",
    "    from srfpython import recompile_src\n",
    "    recompile_src(yes=False)\n",
    "\n",
    "    # verify once more\n",
    "    try:\n",
    "        check_herrmann_codes()\n",
    "        print 'sucess'\n",
    "    except Exception:\n",
    "        print 'Error : unexpected failure, make sure you have gfortran installed'\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I/ create a 1-D depth model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MODEL.01\n",
      "MODELTITLE\n",
      "ISOTROPIC\n",
      "KGS\n",
      "FLAT EARTH\n",
      "1-D\n",
      "CONSTANT VELOCITY\n",
      "LINE08\n",
      "LINE09\n",
      "LINE10\n",
      "LINE11\n",
      "H(KM) VP(KM/S) VS(KM/S) RHO(GM/CC) QP QS ETAP ETAS FREFP FREFS\n",
      "0.250000 1.850000 0.860000 2.470000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000\n",
      "0.200000 2.360000 1.100000 2.470000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000\n",
      "0.200000 2.630000 1.240000 2.470000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000\n",
      "0.200000 3.150000 1.470000 2.470000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000\n",
      "0.200000 3.710000 1.730000 2.470000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000\n",
      "0.480000 4.540000 2.130000 2.580000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000\n",
      "0.270000 5.480000 3.130000 2.580000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000\n",
      "0.000000 5.800000 3.310000 2.630000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# -----------------------\n",
    "# create 1-D depth model using 4 arrays with same length\n",
    "# -----------------------\n",
    "ztop = [0.00, 0.25, 0.45, 0.65, 0.85, 1.05, 1.53, 1.80] #km, top layer depth, positive, increasing downward, 0 = surface\n",
    "vp   = [1.85, 2.36, 2.63, 3.15, 3.71, 4.54, 5.48, 5.80] #km/s, P wave velocity in each layer\n",
    "vs   = [0.86, 1.10, 1.24, 1.47, 1.73, 2.13, 3.13, 3.31] #km/s, S wave velocity in each layer\n",
    "rh   = [2.47, 2.47, 2.47, 2.47, 2.47, 2.58, 2.58, 2.63] #g/cm3, Density in each layer\n",
    "\n",
    "# create the depthmodel object, use a subclass that is to be intitiated with arrays\n",
    "# see also depthmodel, depthmodel1D, depthmodel_from_mod96, ...\n",
    "dm = depthmodel_from_arrays(ztop, vp, vs, rh)\n",
    "\n",
    "\n",
    "# __str__ returns the file content at mod96 format, (see Herrmann CPS documentation)\n",
    "print dm "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -----------------------\n",
    "# write the depth model as a file at mod96 format (see Herrmann CPS documentation)\n",
    "# -----------------------\n",
    "dm.write96('dmtuto.mod96')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 144x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -----------------------\n",
    "# display the depth model\n",
    "# -----------------------\n",
    "plt.figure(figsize=(2, 4))\n",
    "dm.show(gca())\n",
    "gca().set_title('figure 1 : dmtuto.mod96')\n",
    "gca().grid(True, linestyle=\":\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II/ compute dispersion curves "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### II.1/ use in a python program"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed time dispersion : 0.014461s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -----------------------\n",
    "# compute dispersion curves from the depthmodel above\n",
    "# -----------------------\n",
    "\n",
    "# define the dipsersion curves to compute\n",
    "f = freqspace(0.2, 3.5, 35, \"log\")\n",
    "curves = [Curve(wave='R', type='U', mode=0, freqs=f),\n",
    "          Curve(wave='R', type='U', mode=1, freqs=f),\n",
    "          Curve(wave='R', type='C', mode=0, freqs=f),\n",
    "          Curve(wave='R', type='C', mode=1, freqs=f),\n",
    "          Curve(wave='L', type='U', mode=0, freqs=f),\n",
    "          Curve(wave='L', type='U', mode=1, freqs=f),\n",
    "          Curve(wave='L', type='C', mode=0, freqs=f),\n",
    "          Curve(wave='L', type='C', mode=1, freqs=f)] \n",
    "\n",
    "# initiate the dispersion operator\n",
    "hc = HerrmannCaller(curves)\n",
    "\n",
    "# compute dispersion curves\n",
    "with Timer('dispersion'):\n",
    "    # call the dispersion operator for a given depth model (defined above)\n",
    "    curves = hc(ztop=ztop, vp=vp, vs=vs, rh=rh)\n",
    "\n",
    "# display the results\n",
    "ax = plt.subplot(111, xscale=\"log\", yscale=\"log\")\n",
    "for curve in curves:\n",
    "    curve.plot(ax, '.-')\n",
    "logtick(ax, \"xy\")\n",
    "\n",
    "ax.set_title('figure 2 : Herrmann.py demo')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### II.2/ use in command line "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dmtuto.mod96 => dmtuto.surf96\r\n"
     ]
    }
   ],
   "source": [
    "# -----------------------\n",
    "# compute dispersion curves, and save as surf96 file\n",
    "# -----------------------\n",
    "\n",
    "!rm -f dmtuto*.surf96\n",
    "\n",
    "!m96 --disp dmtuto.mod96 \\\n",
    "    -LC0 .1 10 30 plog \\\n",
    "    -RC1 .1 10 30 plog \\\n",
    "    -RU0 .1 10 30 plog \\\n",
    "    -save dmtuto.surf96"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dmtuto.surf96\n",
      "    Claw wave=L mode=0 type=C flag=T extrapmode=0 N=30\n",
      "    Claw wave=R mode=1 type=C flag=T extrapmode=1 N=22\n",
      "    Ulaw wave=R mode=0 type=U flag=T extrapmode=0 N=30\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -----------------------\n",
    "# display output\n",
    "# -----------------------\n",
    "\n",
    "%run -i ../../srfpython/bin/s96 --show dmtuto.surf96 -inline\n",
    "# in terminal, just use m96 --show dmtuto.surf96"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "see also programs **s96** and **m96** that provide more manipulation tools for depth models and surface wave dispersion curves in command line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m96\n",
      "--show            list of mod96 files to display (same plot)\n",
      "--disp            name of mod96 file to use as input\n",
      "    -RU0          rayleigh, group, mode 0 : expects 4 frequency arguments : fstart, fend, nfreq, fscale\n",
      "    -RU1          rayleigh, group, mode 1 : expects 4 frequency arguments : fstart, fend, nfreq, fscale\n",
      "    -RC0          rayleigh, phase, mode 0 : expects 4 frequency arguments : fstart, fend, nfreq, fscale          \n",
      "    -LC0          love,     phase, mode 0 : expects 4 frequency arguments : fstart, fend, nfreq, fscale\n",
      "    ...\n",
      "    -save         name of surf96file to write\n",
      "--split \n",
      "    -thck         thickness of the sublayers in km\n",
      "    -sfx          suffix to add before file extension (default split)\n",
      "    -o            ignore suffix and overwrite input file\n",
      "--addlayer \n",
      "    -thck         thickness of the sublayers in km\n",
      "    -sfx          suffix to add before file extension (default split)\n",
      "    -o            ignore suffix and overwrite input file    \n",
      "-inline           replace showme by plt.show (e.g. for jupyter)\n",
      "\n",
      "s96\n",
      "--show            list of surf96files to display\n",
      "    -freq         plot in frequency domain if specified\n",
      "--resamp          list of surf96files to resample\n",
      "    -fspace       new frequency array in Hz, fstart, fend, nfreq, fscale\n",
      "    -sfx          file suffix to append, use \"\" to overwrite input files\n",
      "-inline             replace showme by plt.show (e.g. for jupyter)\n",
      "#surf96 format \n",
      "SURF96 {wave} {type} {flag} {mode} {period(s)} {value(km/s)} {dvalue(km/s)}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!m96 --help\n",
    "!s96 --help"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III/ depth inversion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### III.1/ Program HerrMet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HerrMet V6.0\r\n",
      "# ------- main options (s=string, i=int, f=float)\r\n",
      "-version, -v          version number, quit\r\n",
      "-help, -h   [s...]    help, provide plugin names for details, quit\r\n",
      "-example, -ex s [s..] example usage, provide plugin names for details, quit\r\n",
      "-w           i        number of virtual workers, default None\r\n",
      "-taskset     s        affinity, e.g. \"0-3\", default None\r\n",
      "-lowprio              run processes with low priority if mentioned\r\n",
      "-verbose     i        reduce verbosity, 0/1, default 1\r\n",
      "# ------- plugins, use --help plugin [plugin ...] for details\r\n",
      "--target     set the target data, create temporary directories (rootnames)\r\n",
      "--param      create a template parameterization file\r\n",
      "--send       send the parameterization to the temporary directories\r\n",
      "--run        invert dispersion data using the Markov Chain Monte Carlo method\r\n",
      "--manage     summarize run file content, manage run results\r\n",
      "--neldermead NOT READY, optimize best solutions from run using the nelder mead algorithm\r\n",
      "--extract    compute and write posterior pdf\r\n",
      "--display    display target, parameterization, solutions\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "# display the main help\n",
    "!HerrMet -help"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### III.2/ Application"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I invert the synthetic data created in section I (dmtuto.surf96) and compare the inversion result to the actual model used to synthetize the data (i.e. dmtuto.mod96)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III.2.1/ Target data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--target     s [s..] set the target dispersion curve(s) from surf96 file(s) (not modified)\r\n",
      "                     for each target, I create a directory in . for temporary files\r\n",
      "                     the data will be reproduced into a target file that can be customized manually\r\n",
      "                     (to remove unwanted points, resample dispersion curves...) \r\n",
      "    -resamp  f f i s resample the dispersion curve in the target file, \r\n",
      "                     requires fmin(Hz), fmax(Hz), nfreq, fscale \r\n",
      "                     (flin=linear in freq domain, plin=linear in period or log=logarithmic scaling)\r\n",
      "    -lunc    f       set constant uncertainties in log domain (uncertainty = value x lunc)\r\n",
      "    -unc     f       set constant uncertainty in linear domain (uncertainty = unc)\r\n",
      "    -ot              force overwriting _HerrMet.target if exists\r\n",
      "    \r\n"
     ]
    }
   ],
   "source": [
    "# display detailed help for one or more plugins\n",
    "!HerrMet -help target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "writing _HerrMet_dmtuto/_HerrMet.target\n",
      "please keep only datapoints to invert in */_HerrMet.target\n",
      "use option --display to see the target data\n",
      "call option --param to see prior depth boundaries\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -----------------------\n",
    "# set the dispersion curves to invert referred to as the target data\n",
    "# the dispersion curve must be provided as surf96 format (see Herrmann's doc, CPS)\n",
    "# I reproduce the file content into _Herrmann.target (also at surf96 format), \n",
    "# which can be modified manually before inversion \n",
    "# (e.g. resample, remove data points or modes, adjust uncertainties for weighting, ...)\n",
    "# -----------------------\n",
    "\n",
    "# the following command will : (see HerrMet --help for command names)\n",
    "# - get the target dispersion curves from dmtuto.surf96 (--target), \n",
    "# - resample it between 0.25-1 Hz with 7 samples spaced logarithmically in period domain (-resamp),\n",
    "# - adjust the uncertainties to 0.2 * velocity (i.e. constant uncertainty in logaritmic domain, --lunc), \n",
    "# - overwrite the target file if exists (-ot) \n",
    "\n",
    "!HerrMet \\\n",
    "    --target dmtuto.surf96 \\\n",
    "        -resamp 0.25 1.0 7 plog \\\n",
    "        -lunc 0.2 \\\n",
    "        -ot\n",
    "\n",
    "%run -i ../../srfpython/bin/HerrMet --display -inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III.2.2/ Parameterization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--param      i f     generate a template parameter file to custom in .\r\n",
      "                     need the number of layers and bottom depth in km\r\n",
      "    -basedon s       build parametrization based on an existing mod96 file, require a filename, \r\n",
      "                     if not specified, I take fixed values to build the parameter file\r\n",
      "    -t       s       parameterization type to use (['mZVSPRRH', 'mZVSVPRH', 'mZVSPRzRHvp', 'mZVSPRzRHz', 'mZVSVPvsRHvp']), \r\n",
      "                     default mZVSPRRH\r\n",
      "          mZVSPRRH = parameterize with \r\n",
      "                     - depth interface (mZ1 = first interface, mZ2, ...), \r\n",
      "                     - VS in each layer (VS0 = first layer, ...), \r\n",
      "                     - VP/VS in each layer (PR0, PR1, ...), \r\n",
      "                     - Density in each layer (RH0, RH1, ...)\r\n",
      "          mZVSVPRH = parameterize with  \r\n",
      "                     - depth interface (mZ1 = first interface, mZ2, ...), \r\n",
      "                     - VS in each layer (VS0 = first layer, ...), \r\n",
      "                     - VP in each layer (VP0, VP1, ...), \r\n",
      "                     - Density in each layer (RH0, RH1, ...)\r\n",
      "       mZVSPRzRHvp = parameterize with  \r\n",
      "                     - depth interface (mZ1 = first interface, mZ2, ...), \r\n",
      "                     - VS in each layer (VS0 = first layer, ...), \r\n",
      "                     - use a fixed relation between VP/VS = f(z)\r\n",
      "                     - use a fixed relation between RH = f(VP)\r\n",
      "        mZVSPRzRHz = parameterize with  \r\n",
      "                     - depth interface (mZ1 = first interface, mZ2, ...), \r\n",
      "                     - VS in each layer (VS0 = first layer, ...), \r\n",
      "                     - use a fixed relation between VP/VS = f(z)\r\n",
      "                     - use a fixed relation between RH = f(z) \r\n",
      "      mZVSVPvsRHvp = parameterize with                     \r\n",
      "                     - depth interface (mZ1 = first interface, mZ2, ...), \r\n",
      "                     - VS in each layer (VS0 = first layer, ...),\r\n",
      "                     - use a fixed relation between VP = f(VS)\r\n",
      "                     - use a fixed relation between RH = f(VP)\r\n",
      "    -dvp     f f     add prior constraint on the vp offset between layers, \r\n",
      "                     requires the extremal values, km/s\r\n",
      "    -dvs     f f     add prior constraint on the vs offset between layers, idem\r\n",
      "    -drh     f f     add prior constraint on the density offset between layers, idem, g/cm3\r\n",
      "    -dpr     f f     add prior constraint on the vp/vs offset between layers, idem, no unit\r\n",
      "    -growing         shortcut for -dvp 0. 5. -dvs 0. 5. -drh 0. 5. -dpr -5. 0.\r\n",
      "    -op              force overwriting ./_HerrMet.param if exists\r\n",
      "    \r\n"
     ]
    }
   ],
   "source": [
    "# display detailed help for one or more plugins\n",
    "!HerrMet -help param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#met DRHMIN = 0.0\n",
      "#met DPRMAX = 0.0\n",
      "#met DVPMIN = 0.0\n",
      "#met DRHMAX = 5.0\n",
      "#met DVPMAX = 5.0\n",
      "#met DVSMAX = 5.0\n",
      "#met DPRMIN = -5.0\n",
      "#met PRIORTYPE = 'DVPDVSDRH'\n",
      "#met NLAYER = 4\n",
      "#met TYPE = 'mZVSPRRH'\n",
      "#met DVSMIN = 0.0\n",
      "#fld KEY VINF VSUP\n",
      "#unt - - -\n",
      "#fmt %5s %16f %16f\n",
      "       -Z1        -0.217990        -0.217990\n",
      "       -Z2        -0.895334        -0.895334\n",
      "       -Z3        -3.000000        -3.000000\n",
      "       VS0         0.860000         0.860000\n",
      "       VS1         1.281411         1.281411\n",
      "       VS2         2.901682         2.901682\n",
      "       VS3         3.310000         3.310000\n",
      "       PR0         2.151163         2.151163\n",
      "       PR1         2.137674         2.137674\n",
      "       PR2         1.832729         1.832729\n",
      "       PR3         1.752266         1.752266\n",
      "       RH0         2.470000         2.470000\n",
      "       RH1         2.470000         2.470000\n",
      "       RH2         2.600424         2.600424\n",
      "       RH3         2.630000         2.630000\n",
      "\n",
      "please customize _HerrMet.param, do not change line orders and metadata\n",
      "use option --display to see the depth boundaries\n",
      "no target file found in .\n",
      "Warning : all parameters are locked\n",
      "parameter type :  Parameterizer_mZVSPRRH\n",
      "prior type     :  LogRhoM_DVPDVSDRH\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/max/anaconda3/envs/py2-dev/lib/python2.7/site-packages/matplotlib/axes/_base.py:3152: UserWarning: Attempting to set identical left==right results\n",
      "in singular transformations; automatically expanding.\n",
      "left=1.0, right=1.0\n",
      "  'left=%s, right=%s') % (left, right))\n",
      "/home/max/anaconda3/envs/py2-dev/lib/python2.7/site-packages/matplotlib/axes/_base.py:3471: UserWarning: Attempting to set identical bottom==top results\n",
      "in singular transformations; automatically expanding.\n",
      "bottom=1.0, top=1.0\n",
      "  'bottom=%s, top=%s') % (bottom, top))\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -----------------------\n",
    "# set the parameter file, the parameters will be stored into _Herrmet.param\n",
    "# use Herrmet to generate a template version\n",
    "# and customize it manually before running the inversion\n",
    "# -----------------------\n",
    "\n",
    "# the following command will :\n",
    "# - set the parameter file with 4 layers down to 3 km (--param)\n",
    "# - the parameters values will be adjusted based on an existing depthmodel (here dmtuto.mod96, -basedon),\n",
    "# - define the parameterization mode as depth, vs, vp/vs and density (-t mZVSPRRH)\n",
    "#   some other modes are available\n",
    "# - require vp, vs and density to be growing (i.e. add prior constraints to the offsets between layers, -growing),             \n",
    "# - overwrite the paramfile if exists (-op) \n",
    "# - display (-display) without pausing (-inline), plot also the actual model (-m96)\n",
    "\n",
    "%run -i ../../srfpython/bin/HerrMet \\\n",
    "        --param 4 3. \\\n",
    "            -basedon dmtuto.mod96 \\\n",
    "            -t  mZVSPRRH \\\n",
    "            -growing \\\n",
    "            -op \\\n",
    "        --display .  \\\n",
    "            -m96 dmtuto.mod96 \\\n",
    "            -inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above figure :\n",
    "> The red dashed curves indicate the prior boundaries (lower and upper limits are equal for now)   \n",
    "> The purple model is the actual model used to generate the synthetic data and to build the parameterization    \n",
    "\n",
    "> * Note that at this step, the boundaries for each parameter (red dashed curves)   \n",
    "> are the same (because VINF=VSUP in _HerrMet.param) : i.e. all parameters are locked  \n",
    "> one need to adjust the VINF, VSUP boundaries for all parameters to invert  \n",
    "> you may do it manually (edit _HerrMet.param),   \n",
    "> here I do it with programming tools for tutorial  \n",
    "\n",
    "\n",
    "> * Note also that VP is not a parameter in this example, (since we use VS and VP/VS)  \n",
    "> so the boundaries displayed on the VP axis are inferred from the VS and VP/VS ones\n",
    ">\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# -----------------------\n",
    "# customize the parameterization file using programming tools (for tutorial)\n",
    "# You may do it manually simply by editing _Herrmet.param\n",
    "# -----------------------\n",
    "\n",
    "#load the parameter file, find lines related to top depth and to VS\n",
    "A = AsciiFile('_HerrMet.param')\n",
    "\n",
    "IZ  = np.asarray([\"Z\"  in _ for _ in A['KEY']], bool) #lines corresponding to Z parameters\n",
    "IVS = np.asarray([\"VS\" in _ for _ in A['KEY']], bool) #lines corresponding to VS parameters\n",
    "IPR2 = np.asarray([\"PR2\" in _ for _ in A['KEY']], bool) #line corresponding to VP/VS in the third layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#met DRHMIN = 0.0\n",
      "#met DPRMAX = 0.0\n",
      "#met DVPMIN = 0.0\n",
      "#met DRHMAX = 5.0\n",
      "#met DVPMAX = 5.0\n",
      "#met DVSMAX = 5.0\n",
      "#met DPRMIN = -5.0\n",
      "#met PRIORTYPE = 'DVPDVSDRH'\n",
      "#met NLAYER = 4\n",
      "#met TYPE = 'mZVSPRRH'\n",
      "#met DVSMIN = 0.0\n",
      "#fld KEY VINF VSUP\n",
      "#unt - - -\n",
      "#fmt %5s %16f %16f\n",
      "       -Z1        -0.310000        -0.110000\n",
      "       -Z2        -1.500000        -1.100000\n",
      "       -Z3        -3.100000        -2.000000\n",
      "       VS0         0.550000         2.220000\n",
      "       VS1         0.780000         3.150000\n",
      "       VS2         1.530000         4.000000\n",
      "       VS3         1.650000         4.000000\n",
      "       PR0         2.151163         2.151163\n",
      "       PR1         2.137674         2.137674\n",
      "       PR2         1.752000         1.752000\n",
      "       PR3         1.752266         1.752266\n",
      "       RH0         2.470000         2.470000\n",
      "       RH1         2.470000         2.470000\n",
      "       RH2         2.600424         2.600424\n",
      "       RH3         2.630000         2.630000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# change parameter boundaries (decrease VINF and increase VSUP), overwrite _HerrMet.param\n",
    "A['VINF'][IVS] = [0.55, 0.78, 1.53, 1.65]\n",
    "A['VSUP'][IVS] = [2.22, 3.15, 4.00, 4.00]\n",
    "A['VINF'][IZ]  = [-.31, -1.5, -3.1]\n",
    "A['VSUP'][IZ]  = [-.11, -1.1, -2.0]\n",
    "A['VINF'][IPR2] = A['VSUP'][IPR2] = 1.752\n",
    "print A\n",
    "\n",
    "# overwrite the parameterization file\n",
    "A.write('_HerrMet.param')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--send       s [s..] send the custom parameterization file ./_HerrMet.param to the specified rootnames, \r\n",
      "                     default _HerrMet_*\r\n",
      "    -op              force overwriting ./rootname/_HerrMet.param if exists\r\n",
      "    \r\n"
     ]
    }
   ],
   "source": [
    "# -----------------------\n",
    "# send the custom version of the parameterization file to the temporary directory\n",
    "# -----------------------\n",
    "!HerrMet -help send"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cp ./_HerrMet.param _HerrMet_dmtuto/\r\n"
     ]
    }
   ],
   "source": [
    "!HerrMet --send -op"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "parameter type :  Parameterizer_mZVSPRRH\n",
      "prior type     :  LogRhoM_DVPDVSDRH\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -----------------------\n",
    "# display the new version of the parameterization file\n",
    "# -----------------------\n",
    "\n",
    "#note that the boundaries now allow VS and Zop to vary between the black dashed lines\n",
    "#note2 : here VP is not a parameter, the prior boundaries on it are infered from other parameters (VS, VP/VS)\n",
    "%run -i ../../srfpython/bin/HerrMet \\\n",
    "    --display \\\n",
    "        -inline \\\n",
    "        -m96 ./dmtuto.mod96"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III.2.3/ Run inversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--run        s [s..] start inversion for the required rootnames, default _HerrMet_*\r\n",
      "    -mode    s       set the running mode, default skip\r\n",
      "                     restart  : overwrite the current run file(s) if any   \r\n",
      "                     append   : add new models to the exiting run file(s) \r\n",
      "                     skip     : ignore rootnames with existsing run file(s)               \r\n",
      "    -nchain  i       number of chains to use, default 12\r\n",
      "    -nkeep   i       number of models to keep per chain, default 100\r\n",
      "    [use -w option before --run to control the maximum number of chains to run simultaneously]   \r\n",
      "    \r\n"
     ]
    }
   ],
   "source": [
    "!HerrMet -help run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pid 3454's current affinity list: 0-7\n",
      "pid 3454's new affinity list: 0-3\n",
      "parameter type :  Parameterizer_mZVSPRRH\n",
      "prior type     :  LogRhoM_DVPDVSDRH\n",
      "pid 3470's current affinity list: 0-3\n",
      "pid 3470's new affinity list: 0-3\n",
      "pid 3471's current affinity list: 0-3\n",
      "pid 3471's new affinity list: 0-3\n",
      "pid 3473's current affinity list: 0-3\n",
      "pid 3473's new affinity list: 0-3\n",
      "pid 3474's current affinity list: 0-3\n",
      "pid 3474's new affinity list: 0-3\n",
      "pid 3469's current affinity list: 0-3\n",
      "pid 3469's new affinity list: 0-3\n",
      "    dmtuto chain    3  DONE kept  100/  493 fail   29 AK 0.20 MP 0.45 AS 195.57/s BST -2.501622\n",
      "    dmtuto chain    2  DONE kept  100/  497 fail   12 AK 0.20 MP 0.45 AS 184.94/s BST -1.091029\n",
      "    dmtuto chain    1  DONE kept  100/  530 fail   14 AK 0.19 MP 0.33 AS 187.16/s BST -3.688247\n",
      "    dmtuto chain    0  DONE kept  100/  541 fail   30 AK 0.18 MP 0.31 AS 189.55/s BST -0.804376\n",
      "    dmtuto chain    4  DONE kept  100/  530 fail   18 AK 0.19 MP 0.35 AS 183.48/s BST -0.953679\n",
      "    dmtuto chain    5  DONE kept  100/  575 fail   25 AK 0.17 MP 0.23 AS 185.24/s BST -1.495818\n",
      "    dmtuto chain    7  DONE kept  100/  581 fail   22 AK 0.17 MP 0.23 AS 181.47/s BST -0.772646\n",
      "    dmtuto chain    6  DONE kept  100/  638 fail    4 AK 0.15 MP 0.15 AS 173.68/s BST -0.713665\n",
      "    dmtuto chain    8  DONE kept  100/  464 fail   29 AK 0.22 MP 0.57 AS 185.66/s BST -1.234783\n",
      "    dmtuto chain   10  DONE kept  100/  411 fail   14 AK 0.24 MP 0.80 AS 184.78/s BST -2.950121\n",
      "    dmtuto chain    9  DONE kept  100/  536 fail   16 AK 0.18 MP 0.31 AS 181.42/s BST -0.759808\n",
      "    dmtuto chain   11  DONE kept  100/  546 fail    9 AK 0.18 MP 0.29 AS 192.88/s BST -0.857376\n",
      "DONE\n"
     ]
    }
   ],
   "source": [
    "# -----------------------\n",
    "# run the inversion, will load the parameterization and target data,\n",
    "# and generate markov chains to sample the posterior pdf (in parallel)\n",
    "# the models generated by the chains will be stored in a sqlite database (_HerrMet.run)\n",
    "# -----------------------\n",
    "\n",
    "# the following command will :\n",
    "# - run the inversion with 12 markov chains in restart mode (overwrites _HerrMet.run if exists)\n",
    "# - each chain will keep 100 models\n",
    "# - use 4 virtual threads (-w) affected to the first 4 physical threads (-taskset)\n",
    "!HerrMet \\\n",
    "    -w 4 \\\n",
    "    -taskset \"0-3\" \\\n",
    "    -verbose 0 \\\n",
    "    --run \\\n",
    "        -mode restart \\\n",
    "        -nchain 12 \\\n",
    "        -nkeep 100\n",
    "\n",
    "print \"DONE\"\n",
    "        \n",
    "# notations :\n",
    "# kept : the number of models kept by the markov chain over the number of tests\n",
    "# fail : some models can lead to failure of the forward algo (CPS), \n",
    "#        we consider them as models with no image in the dataspace\n",
    "#        the penalty is adjusted to force the chains to move away from these dead ends\n",
    "# AK   : Average keep ratio : the number of models kept / the number of tests\n",
    "#        by default, the proposal pdf is adjusted to maintain this value around .25\n",
    "# MP   : Master proposal : a coefficient to adjust the proposal distance according to AK\n",
    "# AS   : Average speed : the average number of models tested per second and per chain\n",
    "# LI   : Final log likelyhood = the quality of the last model found (not necessarily the best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--manage     s [s..] manage run results for given rootnames, default _HerrMet_*\r\n",
      "     -stats          prints detailed stats for each chain of each runfile \r\n",
      "     -plot   [f]     display the convergence for every chain and every rootname, specify the lower bound\r\n",
      "     -inline         do not pause (jupyter)\r\n",
      "     -delbad f       delete bad models, log likelihood below a given threshold, no default\r\n",
      "     -delchains i [i...] delete one or more chains using their chainid\r\n",
      "      \r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "# -----------------------\n",
    "# see the convergence of the chains, delete chains or bad models\n",
    "# -----------------------\n",
    "!HerrMet -help manage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_HerrMet_dmtuto :     12 chains,   1212 models, worst -217954.032013, best  -0.713665, filesize 0M\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run -i ../../srfpython/bin/HerrMet -verbose 0 --manage -delbad -100. -plot -inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--display   s [s...] display param, target, and run outputs for the required rootnames, default _HerrMet_*\n",
      "                     (use \".\" to see the parameterzation template ./_HerrMet.param from option --param)\n",
      "    -plot  [s i f i] show the best models on the figure, arguments are :  \n",
      "                     first argument = selection mode, last or best\n",
      "                     second argument = highest model number to include (>=0, 0 means all)  \n",
      "                     third argument = lowest log likelihood value to include (<=0.0, 0.0 means all)\n",
      "                     fourth argument = include only one model over \"step\" (>=1)\n",
      "                     default last 100 0.0 1             \n",
      "    -overdisp        recompute dispersion curves of the best models selected with higher resolution\n",
      "    -pdf   [s i f i] compute and show the statistics for the selected models, see -plot for arguments\n",
      "                     default last 0 0.0 1 \n",
      "                     use --extract to save pdf outputs\n",
      "    -png   [i]       save figure as pngfile instead of displaying it on screen, requires dpi, default 100 \n",
      "    -m96    s [s...] append depth model(s) to the plot from mod96 file(s)\n",
      "    -cmap            colormap, default viridis\n",
      "    -compact         display only vs and the dispersion curves, default False\n",
      "    -ftsz  i         set font size, default 10\n",
      "    -inline          do not pause (use in jupyter notebooks)\n",
      "    \n",
      "plot : best, limit 1000, llkmin 0.000000, step 1 retrieved 1000 models in 0.203043s \n",
      "pdf : last, limit 0, llkmin 0.000000, step 1 retrieved 1163 models in 0.230114s \n",
      "Stacker Worker-0007 stacked    119 jobs in 0.058302s\n",
      "Stacker Worker-0001 stacked    185 jobs in 0.087854s\n",
      "Stacker Worker-0008 stacked    111 jobs in 0.059226s\n",
      "Stacker Worker-0005 stacked    134 jobs in 0.069395s\n",
      "Stacker Worker-0003 stacked    170 jobs in 0.086191s\n",
      "Stacker Worker-0004 stacked    149 jobs in 0.074173s\n",
      "Stacker Worker-0006 stacked    135 jobs in 0.066065s\n",
      "Stacker Worker-0002 stacked    160 jobs in 0.078618s\n",
      "parameter type :  Parameterizer_mZVSPRRH\n",
      "prior type     :  LogRhoM_DVPDVSDRH\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -----------------------\n",
    "# display the results, by selecting models in the sqlite database (_HerrMet.run)\n",
    "# -----------------------\n",
    "\n",
    "# the following command will :\n",
    "# - display the best 1000 models found and their image in the dataspace (-plot)\n",
    "# - recompute the dispersion curves with higher resolution (-overdisp)\n",
    "# - compute the median and std of the full population of models (-pdf)\n",
    "# - add the model used to synthetize the data (dmtuto.mod96) for comparison (-m96)\n",
    "!HerrMet -help display\n",
    "%run -i ../../srfpython/bin/HerrMet \\\n",
    "        -verbose 0 \\\n",
    "        --display \\\n",
    "            -plot best 1000 0. 1 \\\n",
    "            -overdisp \\\n",
    "            -pdf \\\n",
    "            -inline \\\n",
    "            -m96 dmtuto.mod96\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the figure above:\n",
    "> The black doted models are the prior boundaries  \n",
    "> The red dispersion curves are the target data  \n",
    "\n",
    "> The colored models are the 1000 best models sorted by increaseing likelyhood (see colorbar)   \n",
    "> The colored dispersion curves = corresponding data, recomputed with higher resolution (overdisp)\n",
    "\n",
    "> The gray models are the median (thick), 16% and 84% aposteriori percentiles (thin) computed from the full population of models\n",
    "> The purple model is the actual model used to generate the synthetic data = expected solution  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III.2.4/ Extract results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--extract    s [s..] rootnames for which to compute and extract pdf, default _HerrMet_*\n",
      "    -pdf   [s i f i] extract posterior distribution of the models, save them as mod96files\n",
      "                     first argument = selection mode, last or best\n",
      "                     second argument = highest model number to include (>=0, 0 means all)  \n",
      "                     third argument = lowest log likelihood value to include (<=0.0, 0.0 means all)\n",
      "                     fourth argument = include only one model over \"step\" (>=1)\n",
      "                     default last 0 0 1\n",
      "    -top     [i f i] extract best models \n",
      "                     first argument = highest model number to include (>=0, 0 means all)\n",
      "                     second argument = lowest log likelihood value to include (<=0.0, 0.0 means all)  \n",
      "                     third argument = include only one model over \"step\" (>=1)\n",
      "                     default 10 0.0 1\n",
      "                     \n",
      "extract : llkmin 0.000000, limit 1000, step 1 retrieved 1000 models in 0.198162s \n",
      "Stacker Worker-0007 stacked    108 jobs in 0.055487s\n",
      "Stacker Worker-0002 stacked    159 jobs in 0.079863s\n",
      "Stacker Worker-0003 stacked    132 jobs in 0.069907s\n",
      "Stacker Worker-0005 stacked    102 jobs in 0.050141s\n",
      "Stacker Worker-0001 stacked    167 jobs in 0.082461s\n",
      "Stacker Worker-0004 stacked    114 jobs in 0.061885s\n",
      "Stacker Worker-0006 stacked    117 jobs in 0.059942s\n",
      "Stacker Worker-0008 stacked    101 jobs in 0.052210s\n"
     ]
    }
   ],
   "source": [
    "# -----------------------\n",
    "# HerrMet can create figures, \n",
    "# however you probably need to get the results at numerical format for further analysis\n",
    "# use option --extract to compute and save the posterior pdf purcentiles\n",
    "# -----------------------\n",
    "\n",
    "# the following command will:\n",
    "# -compute the median and std of the best 1000 models found, \n",
    "# -save it as mod96 files named \n",
    "#     _HerrMet.p0.16.mod96,_HerrMet.p0.50.mod96 and _HerrMet.p0.84.mod96\n",
    "!HerrMet -help extract\n",
    "%run -i ../../srfpython/bin/HerrMet \\\n",
    "    -verbose 0 \\\n",
    "    --extract \\\n",
    "        -pdf best 1000 0.0 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_HerrMet_dmtuto/_HerrMet.best_1000_0.0_1.p0.16.mod96\n",
      "_HerrMet_dmtuto/_HerrMet.best_1000_0.0_1.p0.84.mod96\n",
      "_HerrMet_dmtuto/_HerrMet.best_1000_0.0_1.p0.50.mod96\n",
      "dmtuto.mod96\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#display models from --extract using m96\n",
    "%run -i ../../srfpython/bin/m96 --show _HerrMet_*/_HerrMet.best*p*.mod96 dmtuto.mod96 -inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./00_how_to_use_srfpython.ipynb ./readme.txt ./00_how_to_use_srfpython.html\n"
     ]
    }
   ],
   "source": [
    "# clear temporary files\n",
    "import glob, os\n",
    "os.system('rm -rf dmtuto.*96 _HerrMet*')\n",
    "for _ in glob.iglob('./*'):\n",
    "    print _,"
   ]
  }
 ],
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